The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification

Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts. We combine these attentions to train domain-specific deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using expensive annotations like bounding box or part information from end-to-end. The weak supervision constraint makes our work easier to generalize. We have verified the effectiveness of the method on the subsets of ILSVRC2012 dataset and CUB200_2011 dataset. Our pipeline delivered significant improvements and achieved the best accuracy under the weakest supervision condition. The performance is competitive against other methods that rely on additional annotations

GRO J1655-40: from ASCA and XMM-Newton Observations

We have analysed four ASCA observations (1994--1995, 1996--1997) and three XMM-Newton observations (2005) of this source, in all of which the source is in high/soft state. We modeled the continuum spectra with relativistic disk model kerrbb, estimated the spin of the central black hole, and constrained the spectral hardening factor f_col and the distance. If kerrbb model applies, for normally used value of f_col, the distance cannot be very small, and f_col changes with observations.Comment: 2 pages, 1 figure, Conference proceedings to appear in "The Central Engine of Active Galactic Nuclei", ed. L. C. Ho and J.-M. Wang (San Francisco: ASP

In the Name of Fairness: Assessing the Bias in Clinical Record De-identification

Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is often achieved through the use of machine learning algorithms by many commercial and open-source systems. While these systems have shown compelling results on average, the variation in their performance across different demographic groups has not been thoroughly examined. In this work, we investigate the bias of de-identification systems on names in clinical notes via a large-scale empirical analysis. To achieve this, we create 16 name sets that vary along four demographic dimensions: gender, race, name popularity, and the decade of popularity. We insert these names into 100 manually curated clinical templates and evaluate the performance of nine public and private de-identification methods. Our findings reveal that there are statistically significant performance gaps along a majority of the demographic dimensions in most methods. We further illustrate that de-identification quality is affected by polysemy in names, gender context, and clinical note characteristics. To mitigate the identified gaps, we propose a simple and method-agnostic solution by fine-tuning de-identification methods with clinical context and diverse names. Overall, it is imperative to address the bias in existing methods immediately so that downstream stakeholders can build high-quality systems to serve all demographic parties fairly.Comment: Accepted by FAccT 2023; updated appendix with the de-identification performance of GPT-

The Operational Logic and Resolution of Dilemmas Driving Urban Cultural Tourism Development through Ice and Snow Sports: A Case Study of Harbin

Since the macro goal of “300 million people joining in ice and snow sports activities” was put forward, and the purpose was to advocate Winter Olympics, familiar winter sports have entered the public domain. The baton of “net-famous cities” has regained prosperous vitality into Harbin, an old-fashioned heavy industry city in Northeast China. The city has gained widespread attention among public, not only propelling Harbin to a prominent position on the tourism radar, but also triggering numerous hot topics. Under the guidance of various levels of policy, the new business models of ice and snow culture and distinctive sightseeing in Harbin will undoubtedly cultivate a nationwide sports atmosphere. With the upcoming hosting of multiple domestic events, the commercial value of urban sports events is also improving. However, tourism popularity always experiences gradual ebb after its illustrious peak with the weakening of public attention and enthusiasm. How Harbin can deeply tap into its own natural advantages when public interest diminishes, develop characteristic winter events. It is worthwhile to consider how to integrate snow and ice-related sporting events with metropolitan tourist attractions in an environmentally sound way

Improved Federated Learning for Handling Long-tail Words

Automatic speech recognition (ASR) machine learning models are deployed on client devices that include speech interfaces. ASR models can benefit from continuous learning and adaptation to large-scale changes, e.g., as new words are added to the vocabulary. While federated learning can be utilized to enable continuous learning for ASR models in a privacy preserving manner, the trained model can perform poorly on rarely occurring, long-tail words if the distribution of data used to train the model is skewed and does not adequately represent long-tail words. This disclosure describes federated learning techniques to improve ASR model quality when interpreting long-tail words given an imbalanced data distribution. Two different approaches - probabilistic sampling and client loss weighting - are described herein. In probabilistic sampling, the federated clients that include fewer long-tail words are less likely to be selected during training. In client loss weighting, incorrect predictions on long-tail words are more heavily penalized than for other words

Modeling, Simulation and Implementation of a Bird-Inspired Morphing Wing Aircraft

We present a design of a bird-inspired morphing wing aircraft, including bionic research, modeling, simulation and flight experiments. Inspired by birds and activated by a planar linkage, our proposed aircraft has three key states: gliding, descending and high-maneuverability. We build the aerodynamic model of the aircraft and analyze its mechanisms to find out a group of optimized parameters. Furthermore, we validate our design by Computational Fluid Dynamics (CFD) simulation based on Lattice-Boltzmann technology and determine three phases of the planar linkage for the three states. Lastly, we manufacture a prototype and conduct flight experiments to test the performance of the aircraft.Comment: 2019 3rd International Conference on Robotics and Automation Sciences (ICRAS